ORCID ID

https://orcid.org/0000-0002-2297-6181

Date Awarded

2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.)

Department

Applied Science

Advisor

Mark K. Hinders

Committee Member

Hannes Schniepp

Committee Member

Walter Silva

Committee Member

Nathanael Kidwell

Abstract

We describe a framework for global shipping container monitoring using machine learning with multi-sensor hubs and infrared catadioptric imaging. A wireless mesh radio satellite tag architecture provides connectivity anywhere in the world which is a significant improvement to legacy methods. We discuss the design and testing of a low-cost long-wave infrared catadioptric imaging device and multi-sensor hub combination as an intelligent edge computing system that, when equipped with physics-based machine learning algorithms, can interpret the scene inside a shipping container to make efficient use of expensive communications bandwidth. The histogram of oriented gradients and T-channel (HOG+) feature as introduced for human detection on low-resolution infrared catadioptric images is shown to be effective for various mirror shapes designed to give wide volume coverage with controlled distortion. Initial results for through-metal communication with ultrasonic guided waves show promise using the Dynamic Wavelet Fingerprint Technique (DWFT) to identify Lamb waves in a complicated ultrasonic signal.

DOI

https://doi.org/10.21220/9j2v-ek78

Rights

© The Author

Included in

Physics Commons

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